Update README.md

README.md

@@ -1,54 +1,37 @@
 ---
 language: en
-license: apache-2.0
+license: mit
 library_name: diffusers
-tags: []
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
 datasets: yuntian-deng/im2latex-100k
 metrics: []
 ---
 
-<!-- This model card has been generated automatically according to the information the training script had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # latex2im_ss_finetunegptneo
 
 ## Model description
 
-This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library 
-on the `yuntian-deng/im2latex-100k` dataset.
-
-## Intended uses & limitations
-
-#### How to use
-
-```python
-# TODO: add an example code snippet for running this diffusion pipeline
-```
-
-#### Limitations and bias
-
-[TODO: provide examples of latent issues and potential remediations]
-
-## Training data
-
-[TODO: describe the data used to train the model]
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 16
-- eval_batch_size: 16
-- gradient_accumulation_steps: 1
-- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
-- lr_scheduler: None
-- lr_warmup_steps: 500
-- ema_inv_gamma: None
-- ema_inv_gamma: None
-- ema_inv_gamma: None
-- mixed_precision: no
-
-### Training results
-
-📈 [TensorBoard logs](https://huggingface.co/yuntian-deng/latex2im_ss_finetunegptneo/tensorboard?#scalars)
-
+Details of this model can be found in [our paper on markup-to-image generation](https://arxiv.org/pdf/2210.05147.pdf). Our code is built on top of HuggingFace [diffusers](https://github.com/huggingface/diffusers) and [transformers](https://github.com/huggingface/transformers).
+
+## Online Demo: [https://huggingface.co/spaces/yuntian-deng/latex2im](https://huggingface.co/spaces/yuntian-deng/latex2im).
+
+## Model Details
+- **Developed by:** Yuntian Deng, Noriyuki Kojima, Alexander M. Rush
+- **Model type:** Diffusion-based text-to-image generation model
+- **Language(s):** English
+- **License:** [MIT](https://github.com/da03/markup2im/blob/main/LICENSE).
+- **Model Description:** This is a model that can be used to generate math formula images based on LaTeX prompts.
+- **Resources for more information:** [GitHub Repository](https://github.com/da03/markup2im), [Paper](https://arxiv.org/abs/2210.05147).
+- **Cite as:**
+
+      @inproceedings{
+        deng2023markuptoimage,
+        title={Markup-to-Image Diffusion Models with Scheduled Sampling},
+        author={Yuntian Deng and Noriyuki Kojima and Alexander M Rush},
+        booktitle={The Eleventh International Conference on Learning Representations },
+        year={2023},
+        url={https://openreview.net/forum?id=81VJDmOE2ol}
+      }